LocDreamer: World Model-Based Learning for Joint Indoor Tracking and Anchor Scheduling

TL;DR

LocDreamer introduces a world model-based approach for joint indoor tracking and anchor scheduling, significantly reducing resource consumption while maintaining high accuracy through synthetic data generation and reinforcement learning.

Contribution

It presents a novel world model framework that enables synthetic measurement generation for efficient training of tracking and scheduling models in indoor localization.

Findings

Outperforms Bayesian filter with random scheduling by 37% in accuracy

Achieves 86% of real-data trained model accuracy

Enhances data efficiency and generalization in indoor tracking

Abstract

Accurate, resource-efficient localization and tracking enables numerous location-aware services in next-generation wireless networks. However, existing machine learning-based methods often require large labeled datasets while overlooking spectrum and energy efficiencies. To fill this gap, we propose LocDreamer, a world model (WM)-based framework for joint target tracking and scheduling of localization anchors. LocDreamer learns a WM that captures the latent representation of the target motion and localization environment, thereby generating synthetic measurements to imagine arbitrary anchor deployments. These measurements enable imagination-driven training of both the tracking model and the reinforcement learning (RL)-based anchor scheduler that activates only the most informative anchors, which significantly reduce energy and signaling costs while preserving high tracking accuracy. Experiments on a real-world indoor dataset demonstrate that LocDreamer substantially improves data efficiency and generalization, outperforming conventional Bayesian filter with random scheduling by 37% in tracking accuracy, and achieving 86% of the accuracy of same model trained directly on real data.